October 27, 2025
Artificial Intelligence (AI) pilots can be fun experiments, but if they never progress into production, they’re just that – experiments.
Across Australia, banks, superannuation funds and other financial institutions are increasingly recognising that AI can unlock significant opportunities across improving customer experiences, enhancing risk management and automating regulatory compliance processes. Yet, while enthusiasm remains high, many organisations are stuck in the pilot stage.
At a recent financial services roundtable, CTOs and CIOs told us that they’ve tested ideas, run proof of concepts and seen the potential of AI, but they haven’t been able to move AI into production, where it really begins to deliver measurable impact. Scaling processes, maintaining quality outputs and the simple “fear of getting it wrong” remain significant blockers.
To move beyond experiments and start embedding AI responsibly and effectively across an organisation, particularly within heavily regulated sectors such as financial services, technology leaders need to go back to the beginning and start on the right track with AI.
AI is an incredibly powerful new tool, but it is still just that – a tool. The temptation to use AI simply because it is new and exciting, with immense capabilities and potential, can lead organisations down the wrong path. The first step is to identify business problems and then assess whether AI is truly the best way to solve them.
By mapping business priorities and pinpointing specific use cases within banking and finance, organisations can trial AI adoption in areas where it will add value to the business. It can also highlight where problems can be solved more easily through simpler, existing means, such as automation or process changes, reducing wasted effort implementing AI where it isn’t needed.
AI can even play a valuable role in this prioritisation process. At a recent AI discovery workshop with a client in the financial sector, we asked participants to interact with an AI chat assistant to share their ideas and challenges in advance. Through a series of guided prompts based on business priorities, the AI tool gathered insights and automatically categorised them on a Miro board. It even read the prompts aloud in the voice of the technology leader for an extra touch of branding.
By the time the workshop began, participants had already seen AI in action and were aligned in key themes. The discussions moved quickly, and the group surfaced three high-priority use cases that could be quickly translated into business cases ready for development in the near future.
AI is also a powerful accelerator in ideation and validation. Used well, it allows teams to explore multiple solutions, create prototypes faster and quickly test what works. Even if these prototypes are ultimately discarded, being able to demonstrate ideas and iterate almost in real time helps build momentum and AI buy-in.
By using AI as a catalyst for insight and decision making, and not just development, organisations can start seeing real value from the outset.
Many organisations talk about “doing AI”, but often these projects aren’t structured to ever make it into production. The pilot becomes the destination rather than another step on the path to operationalisation.
To add real value, AI pilots should test the viability of a concept while also laying the required groundwork for scale in the future. It’s not enough to prove that something works in isolation. You need to understand how it will perform in a live environment, with real data, users and business constraints.
That’s where guardrails come in. During the pilot stage, you’re operating in a controlled environment with limited datasets, small user groups and defined goals. But what happens when you move that AI capability into production? Will it still function as expected, or will issues with data quality and processes threaten to derail it?
Instead, organisations should design pilots with the clear intention that they will eventually be scaled. By building in tight constraints and considering foundational production requirements, such as data governance and regulatory compliance, at this early stage, organisations can start to build trust and readiness. In highly-regulated sectors like banking, this level of discipline is non-negotiable.
We’ve been supporting financial services, banking, and superannuation organisations to apply this thinking in their approach to adopting AI within the software development lifecycle (SDLC). At the pilot stage, teams must be able to interrogate what AI produces before determining if it’s ready for broader production and scale across a team. To do this, organisations need people with the right skills to understand what “good” looks like as well as the ability to test, question, and confirm the results.
By embedding this discipline early, through defined AI boundaries, human oversight and ethical, compliant design, organisations can be confident that when AI moves into production, it’s robust, trustworthy, and scalable.
Building a working proof of concept is one thing, but operationalising AI at scale in an organisation is something else entirely. AI isn’t a bolt-on technology; it requires change across the organisation, including people, process and culture.
Moving from pilot to production means embedding AI into the fabric of your organisation and ensuring the foundational elements are in place. This can include:
Overcoming fear and hesitancy is also critical at this stage. We’ve seen some promising AI pilots in financial services stall at the edge of production due to concerns over risk. Transparency is key to addressing this. Being able to show stakeholders that robust guardrails are in place, from data governance to ethical oversight, and demonstrate the measurable business value of AI can start to change hearts and minds.
Operationalising AI effectively also means thinking beyond single use cases. Rather than scaling pilots, organisations should look to scale patterns. Create repeatable practices through playbooks, prompt libraries and reusable frameworks that make AI adoption consistent, safe, and effective.
Equal Experts worked with the UK Government’s Department for Environment, Food and Rural Affairs (Defra), to demonstrate how AI can responsibly enhance software delivery across the organisation without compromising quality, ethics, or compliance. With our support, Defra was able to embed AI into core activities, including code generation, testing, backlog refinement and technical analysis.
At the heart of the engagement was the creation of the Defra Playbook, a structured, evolving guide to integrating AI into software engineering workflows responsibly. It provided Defra with early proof that disciplined, structured AI adoption can drive meaningful impact, and the playbook remains a critical first step towards a more AI-integrated operating model. Read more about our work with Defra
AI is a powerful tool within software engineering, but it is still a means to an end, not the end itself. For banks and other financial sector businesses looking to move beyond AI pilots and realise real value, success depends on:
If you’re ready to move beyond pilots and start delivering measurable business value through AI, we can help. Contact us today to book an AI Discovery Workshop with our team to identify high-impact use cases, prioritise your roadmap, and take the first steps toward operationalising AI responsibly and effectively.
Andy Canning is the Chief Technology Officer and Managing Director for Equal Experts APAC. With a technology career that spans the globe, Andy has been at the forefront of innovation for more than 30 years and is on a mission to revolutionise the business landscape through the transformative power of AI. Connect with Andy on LinkedIn
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